Abstract content

Attribution of extreme weather events has recently generated a lot of interest simultaneously within the general public, the scientific community, and stakeholders affected by meteorological extremes. This interest calls for the need to explore the potential convergence of the current atttribution science with the desire and needs of stakeholders. Such an euiry contributes to the development of climate services aiming at quantifying the human responsibility for particular events.

Through interviews with climate scientists (internationally and within Germany), through the analysis of the press coverage of extreme meteorological events (heat wave in the Paris area, storm surges in the baltic sea), and through stakeholder (private sector, covernment services and local and regional government) focus groups, we analyze how social representations of the concepts associated with extreme event attribution are theorized. From the corpuses generated in the course of this enquiry, we build up a grounded, bottom-up, theorization of extreme weather event attribution. This bottom-up theorization allows for a framing of the potential climate services in a way that is attuned to the needs and expectations of the stakeholders.

From apparently simple formulations: “what is an extreme event?”, “what makes it extreme?”, “what is meant by attribution of extreme weather events?”, “what do we want to attribute?”, “what is a climate service?”, we demonstrate the polysemy of these terms and propose ways to address the challenges associated with the juxtaposition of four highly loaded concepts: extreme – event – attribution –climate services.

Abstract details

Abstract content

Extremes of weather and climate events at global and regional scales can have devastating effects on human society and the environment, and there is overwhelming evidence that these extreme events are changing. Interest in attributing the risk of these extreme events to anthropogenic climate change is increasing and understanding these past changes is critical for reliable projections of future changes. However, there is still no consensus about the best methodology for event attribution. A common approach relies on experiments in which the time periods of interest are simulated using an atmospheric general circulation model (AGCM) forced by prescribed sea surface temperatures (SSTs), with and without anthropogenic influences. A potential limitation of these experiments is the lack of explicit atmosphere–ocean coupling, and therefore a key question is whether the attribution statements derived from such studies are in fact robust. In this research, we have carried out climate model experiments to test attribution conclusions in a situation where the answer is known – a so called “perfect model” approach. The study involves comparing attribution conclusions derived from experiments with a coupled climate model (specifically an AGCM coupled to an ocean mixed layer model) with conclusions derived from parallel experiments with the same atmosphere model forced by SSTs taken from the coupled model experiments. We analyse and compare the changes in surface air temperature and precipitation in response to anthropogenic forcing in the coupled and uncoupled experiments, assessing both seasonal mean changes and extreme events. Our experimental design also allows comparison with observed changes. Our results demonstrate that whilst the AGCM method has some strengths, it also has significant limitations and may lead to erroneous attribution conclusions in some situations.

Abstract content

Climate predictions are increasingly being used to attribute single extreme climate events to anthropogenic factors by retrospective predictions. The reliability of the systems to predict extreme events might be an important aspect in such an assessment, yet limited hindcast periods which cover only a small number of extreme events in the past inhibit a robust evaluation. Using an idealized framework based on a synthetic forecast model this limitation is here circumvented. The framework allows to perform large number of predictions and to compute the fraction of attributable risk (FAR) on extreme events. Varying the forecast reliability shows that unreliable climate predictions are prone to overestimate the FAR due to ensemble overconfidence, which leads to unrealistically small probabilities for the event to occur without climate change. We show under which conditions forecast reliability becomes important an factor and give an outlook for future event attribution systems using climate predictions.

Abstract details

Abstract content

In spite of a relatively dry mean climate, the Mediterranean regions in Southern France use to experience heavy rainfalls over short durations - typically a few minutes to one day. If compared to the rest of mainland France, 10-yr return values of maximum daily precipitation are two to four times larger over this area. The 2014 fall season was particularly severe, with 11 events exceeding the 190mm/day threshold, flash floods and several fatalities.

The possible link between such events and anthropogenic climate change has not been specifically addressed so far. Several approaches might be proposed to deal with this issue, including the realisation of specific climate model experiments. Unlike many other studies, we only focus on past observations and investigate the significance of recent trends in such events. Trends are investigated in terms of magnitude, frequency, and extent of events. Some statistical challenges arise to properly account for spatial depencies among locations. While previous studies looked at trends locally or over a small neiborhood, here we propose an aggregated diagnosis for the whole region (about 100 stations). The consistency of these trends with the change simulated by state of the art climate models is also discussed. Main results suggest that observationnally based estimates of the human influence on heavy precipitation events are still weakly constrained over such a limited area.

Abstract content

A severe heat wave occurred in the southwestern United States (US) during June and July 2013. To investigate the effects of natural variability and anthropogenic climate change on this event, we generated large ensemble simulations of possible weather using the MIROC5A climate model forced by “historical external forcing agents, sea surface temperature (SST) observations and sea ice (SIC) observations” both with and without human influence. It was suggested that both the anthropogenic warming and an atmospheric circulation regime related to the natural variability of SST and SIC made the heat wave event more likely. On the other hand, no significant human influence was found in atmospheric circulation patterns. These results were robust for two different estimates of anthropogenic signals on SST and SIC.

Abstract content

Throughout 2014, the regions of Jordan, Israel, Lebanon and Syria have experienced a persistent draught with clear impacts on the local populations. In this study we look at how the probability of such a draught has changed under climate change, with a specific focus on the flow rate of the Litani river and the water level of Lake Tiberious (AKA the Sea of Galilee). Both of which hold major societal, political and religious importance. To perform the analysis we make use of distributed computing power to run thousands of modelled years of 2014 with slightly different initial conditions. We use an atmosphere only model (HadAM3p) with a nested 50 km regional model covering Africa and the Middle East. These data are downscaled to 1 km. Two separate experiments and simulations, 1. For all known climate forcings that are present in 2014, and 2. For a naturalised 2014 scenario where we assume humans never impacted the climate. For observations, we use station data obtained from the Jordanian Ministry of Water. Using a combination of these local station observations and model data we are able to make clear statements on the attribution of a 2014-like draught event to human causal factors.

Abstract content

In the aftermath of the 2013 Blue Mountains wildfires in New South Wales, Australia, the scientific community was faced with the challenge of quantifying the event’s link to different causal factors, including human-induced climate change. While there are a number of recorded attribution studies for temperature and precipitation-related events, no such study exists for fire weather.

This study investigates how the likelihood of extreme fire weather in south-east Australia has been changed due to the competing influences of human-induced climate change and modes of inter-annual climate variability. Our analysis benefits from the use of the recently launched weather@home Australia-New Zealand distributed computing citizen science project to generate very large ensembles of regional climate model simulations over Australia. The likelihood of extreme fire weather is examined for different phases of the El Niño Southern Oscillation under present climate conditions and climate conditions with no human influences.